Reflections from CFTE’s conversation with Dr David R. Hardoon in Abu Dhabi

There is a familiar problem inside many organisations trying to use AI.

The business wants to move quickly.
The technology team wants to experiment.
The governance team wants to understand the risks.
Leadership wants confidence before anything is scaled.

Too often, these teams meet too late.

A use case is built. A pilot is tested. Then governance is asked to approve it. At that point, the conversation can feel like a stop sign: too risky, not clear enough, not ready, not aligned.

But governance should not be where innovation stops.

That was one of the key messages from CFTE’s recent conversation with Dr David R. Hardoon in Abu Dhabi: good governance should help organisations move forward, not hold them back.

The problem is not governance. It is when governance starts.

AI is now part of real work in financial services. It is being used in customer service, fraud detection, compliance, operations, software development, research, productivity, and decision support.

That means governance can no longer be treated as a final approval step.

If governance only comes in at the end, teams are left guessing what is acceptable. They may build something that cannot be used. They may move too slowly. Or worse, they may avoid the process altogether because it feels disconnected from the work.

The better approach is to bring governance in earlier.

Before a team starts building, they should already understand the basic questions:

What data can we use?
What risks are we comfortable with?
Where do we need human oversight?
What happens if the system makes a mistake?
Who is responsible?

These questions do not stop innovation. They make innovation usable.

From “no” to “how”

Dr Hardoon has been part of CFTE’s AI journey since 2018. Back then, the conversation around AI in finance was still mostly about machine learning, data, model governance, and responsible experimentation.

Today, the conversation has changed.

Generative AI has entered daily workflows. Agentic AI is raising new questions about autonomy and oversight. Regulators are looking closely at how institutions balance innovation with trust.

But one message has stayed consistent: governance should not be the department of “no”.

Its role should be to help teams understand how something can be done safely, where the boundaries are, and whether the organisation is ready to proceed.

That is a much more useful way to think about governance.

AI cannot be governed once and forgotten

One reason AI is difficult to govern is that it does not behave like a normal business process.

A traditional process can often be reviewed, approved, documented, and checked again later.

AI is different.

Models change.
Outputs vary.
Users behave differently.
New risks can appear after deployment.

This is especially true when AI systems produce different answers depending on prompts, context, data, or user behaviour.

So governance cannot be a one-time gate. It has to continue after launch.

That does not mean making every AI project slow or heavy. It means having practical ways to monitor what is happening, spot issues early, and know when someone needs to step in.

Done well, governance does not slow teams down. It prevents them from reaching the end of a project and discovering that what they built cannot actually be used.

Agentic AI makes this even more important

The conversation became especially interesting when it turned to agentic AI.

Agentic AI is often described as AI that can act more independently. But for organisations, the bigger issue is not just the AI agent itself. It is the system around it.

In a bank, an AI tool does not operate in isolation. It connects with policies, customer data, internal workflows, human teams, escalation rules, and compliance requirements.

Take a call centre as an example.

The question is not only: did the AI give the right answer?

The better questions are:

Was the answer based on the right information?
Did it follow the bank’s policy?
Did it know when to escalate to a human?
Can the organisation see what happened?
Who is accountable if something goes wrong?

This is why AI governance cannot only focus on the model. It has to look at the whole system.

Governance is also about people

Another important point from the discussion was that governance is not only about policies and controls.

It is also about capability.

Many organisations already have access to AI tools. That is not the main problem.

The bigger challenge is that different teams understand AI in very different ways.

Business teams want speed.
Technology teams want to test.
Risk teams need clarity.
Leaders need confidence.
Employees need to know what responsible use looks like.

When these groups do not share a common language, AI adoption becomes slow and fragmented.

This is why capability building is part of governance.

A policy only works if people know how to apply it. A framework only matters if teams can use it in real decisions. Responsible AI principles only matter if they change how people design, use, monitor, and challenge AI systems.

Why this matters in Abu Dhabi and the UAE

David’s visit to Abu Dhabi reflects a wider priority for CFTE: bringing global experts into the UAE and the Middle East to support the region’s ambitions in AI, finance, and future skills.

The region is not watching the AI conversation from the sidelines.

Governments, regulators, financial institutions, and enterprises are actively investing in AI as a driver of productivity, competitiveness, workforce transformation, and innovation.

But what makes the conversation especially important is the focus on people.

AI adoption is not only about infrastructure, models, or tools. It is about whether leaders, teams, and institutions have the skills and judgement to use AI well.

That is where global expertise needs to meet local ambition.

The right question is not simply: what is global best practice?

A better question is: what is globally informed, locally relevant, and useful in practice?

The human skill that matters most

One of the strongest ideas from the conversation was about human judgement.

As AI becomes more capable, people do not become less important. Their role changes.

People need to become better at asking questions, framing problems, recognising trade-offs, interpreting outputs, and taking responsibility for decisions.

David described cognitive skills as a muscle. That is a useful way to think about AI readiness.

If AI helps more with execution, then human value moves towards judgement.

What should we ask?
What should we trust?
What should we challenge?
What could go wrong?
What does a responsible decision look like?

These are the questions that will determine whether AI creates real value or introduces new risks.

The real work ahead

We are grateful to Dr David R. Hardoon for joining us in Abu Dhabi and for continuing to contribute to CFTE’s work.

His message is timely for financial services and for the region.

Governance and innovation should not sit on opposite sides of the table. Governance should help teams move with more clarity. Innovation should bring governance into the process earlier. Capability building should give people the confidence to make better decisions.

At CFTE, this is why we bring experts like David to the UAE and the wider Middle East.

The aim is not to add another conversation about AI trends. It is to support the people and institutions building the future of finance in practice.

AI governance is not the brake.

Done properly, it is how organisations learn to move faster, safer, and with greater confidence.

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The UK’s AI objectives are changing. It is no longer only a question of ambition, innovation or adoption. It is now a question of implementation.

We are entering the age of agentic AI: systems that do not only assist, but act. They can plan, reason across steps, coordinate with other agents and operate inside the tools organisations already use. Generative AI changed how people create and analyse information. Agentic AI will change how work is organised, how decisions are made and how institutions deliver value.

For the UK, this creates a strategic opening. The country has world-class research, a strong technology ecosystem, sophisticated regulators, deep sector expertise and a government that has placed AI at the centre of national growth. But if we want AI to translate into productivity, trust and inclusive prosperity, the next phase cannot be only about tools, models or infrastructure. It must also be about people.

The UK does not only need AI adoption. It needs AI fluency.

Adoption means access to tools. Fluency means the capability to understand what AI can do, where its limits are, when to trust it, when to challenge it and how to apply it responsibly in context. It is the difference between using AI as a faster task-doer and knowing how to redesign work around intelligent systems.

This distinction matters because agentic AI is not just another software upgrade. When a system can act, leaders must ask new questions. What should be automated? Who authorises an action? Who is accountable for the outcome? How do we audit decisions made or supported by autonomous systems? When should humans intervene? What happens when an agent acts across multiple systems, suppliers or jurisdictions?

These are not purely technical questions. They are questions of operating model, governance, skills and trust.

The opportunity is already visible. In financial services, Lloyds Banking Group has announced an AI-powered financial assistant for millions of customers. In education, the UK government is supporting the development of AI tutoring tools that could help disadvantaged pupils. In public services, police forces are experimenting with digital agents to respond to non-emergency citizen queries and free human teams to focus on higher-value work.

The next frontier is agentic commerce, where agents search, compare, negotiate and buy on behalf of individuals and businesses. For companies, the question will shift from “How do we reach the customer?” to “How do we become discoverable, trusted and chosen by the agent acting for the customer?”

This is the scale of the change ahead. Agentic AI will begin to decouple cognitive work from headcount. Organisations will be able to increase output without increasing people in the same way. Some professionals will be outcompeted by systems that are faster, cheaper and always available. Others will be supercharged, using AI with their judgement, domain expertise and contextual understanding to reach a new level of performance.

The dividing line will not be the technology. It will be capability.

From our work with financial institutions, regulators and governments internationally, we see the same pattern repeatedly. Organisations rarely struggle because they lack access to AI tools. They struggle because their people do not yet have the fluency to redesign workflows, govern risks and create value responsibly.

This is why AI fluency must become a national priority.

If fluency remains concentrated among a small group of technical experts or already advantaged firms, AI will widen the gap between those who can benefit and those who are displaced. But if fluency is built deliberately across sectors, regions and communities, it can become a foundation for broader participation in the AI economy.

For the UK, this requires a human-capital infrastructure for AI.

First, the UK needs a shared understanding of AI fluency. Not everyone needs to become an engineer. But citizens, frontline workers, managers, executives, regulators and board members all need the ability to use, question and govern AI at the right level for their role. A common fluency framework would help organisations move beyond generic awareness and define the capabilities people actually need.

Second, fluency must be built around sectors and roles. AI in banking is not the same as AI in healthcare, education, legal services, manufacturing or public administration. The workflows, risks, accountability structures and professional norms are different. The future belongs to AI-bilingual professionals: bankers, clinicians, civil servants, teachers, regulators and entrepreneurs who understand both their field and how AI transforms it.

Third, capability must be embedded into responsible deployment. Organisations should not be assessed only on whether they use AI, but on whether they have the skills, governance, auditability and oversight to use it well. Procurement, regulation and institutional strategy should reward responsible implementation, not just technological novelty.

Fourth, inclusion must be designed into the system from the start. Women, underrepresented communities, workers outside major technology hubs, SMEs and professionals in roles most exposed to automation must not be treated as an afterthought. AI fluency cannot be a privilege for the few. It must become a capability distributed across the economy.

This is the human-capital layer of AI leadership that has been missing from too many AI strategies. At CFTE, our work has shown that responsible AI adoption is not a one-off training exercise. It is institutional readiness built as a system: defined through proficiency frameworks, delivered through role-based pathways tied to real workflows and measured through capability diagnostics.

Technology will continue to advance. Models will become more powerful, cheaper and more autonomous. But the countries that lead will not simply be those that move fastest. They will be those that build the deepest capacity to adapt, govern and create value responsibly.

For the UK, the real opportunity is not only to be a global AI innovator. It is to become a global model for responsible AI implementation.

That means recognising capability as infrastructure. It means treating AI fluency as seriously as compute, data and regulation. And it means ensuring that the people who power the economy are not passive recipients of technological change, but active shapers of it.

The future of agentic AI will not be determined by technology alone. It will be determined by people. The choice for the UK is whether we shape this shift, or are shaped by it.

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Today, Anthropic released Claude Fable 5 and Claude Mythos 5. This is not just another AI launch. It is one of the clearest signals yet that AI has entered a new phase, and financial services is directly in its path.

The most capable AI model ever made available to the public is now in everyone’s hands, more capable and far cheaper than the model before it. The single most important thing to understand is not the model itself. It is what the model signals. AI capability is now compounding faster than the governance frameworks, risk cycles, operating models, and skills that financial services was built on.

This article explains what launched, what a Mythos-class model is, why it matters for banks, regulators, and professionals, and what to do about it.

What did Anthropic actually launch?

Anthropic released two models. They are easy to confuse, and the difference matters.

Claude Fable 5 is the public model. It is a Mythos-class model that Anthropic has made safe for general use, and the company describes it as the most capable model it has ever made generally available. It is available to everyone today.

Claude Mythos 5 is not public. It is the same underlying model with its cybersecurity safeguards lifted, restricted to a small group of partners through Project Glasswing in collaboration with the US Government, with select biology researchers to follow.

So the headline matters: Mythos itself did not go public. A Mythos-class model, Fable 5, did. That distinction is the difference between a controlled frontier capability and a capability now sitting on every professional’s desk.

What is a Mythos-class model?

A Mythos-class model is a tier of capability that sits above Anthropic’s previous Opus class. The first, Claude Mythos Preview, was released in April 2026 to a limited set of cyber defenders and infrastructure providers because its capabilities were considered too powerful for open release. Fable 5 and Mythos 5 follow it.

According to Anthropic, Fable 5 is state of the art on nearly all tested benchmarks, with particular strength in software engineering, knowledge work, vision, and scientific research. The longer and more complex the task, the larger its lead over previous models. For finance specifically, Anthropic reports that Fable 5 tops Hebbia’s finance benchmark for senior level reasoning, with gains in document based reasoning, chart and table interpretation, and problem solving.

In plain terms: this is not an incremental upgrade. It is a step change in what a single professional can do.

Why is this the signal of exponential AI?

At CFTE we have been describing a shift we call the Third Wave of AI. It is the lens that makes today’s launch legible.

The first wave was machine learning, AI that could predict. The second wave was generative AI, AI that could reason, the moment most people met through ChatGPT. The third wave is AI that can act. It uses tools, runs multi step workflows, and executes in the digital world, not just answering questions but completing work.

Fable 5 is a clear marker of that third wave. Anthropic reports that an early tester, Stripe, used the model to perform a codebase wide migration across a fifty million line codebase in a single day, work that would otherwise have taken a team more than two months by hand.

Three forces are compounding this shift at the same time:

1. AI is becoming sharply better. Capability that was state of the art months ago is now routine, and the hardest, longest tasks are where the newest models pull furthest ahead.
2. AI is becoming sharply cheaper. Fable 5 launched at less than half the price of the model before it, at 10 dollars per million input tokens and 50 dollars per million output tokens.
3. AI is becoming sharply more autonomous. Models now sustain useful work across long, complex tasks rather than short bursts.

When capability rises and cost falls at the same time, year after year, the result is exponential, not linear. That is the real story. The model is only the signal.

What does exponential AI mean for financial services?

Financial services has invested in AI for years. The challenge now is not access to the technology. It is the capability to use it well, govern it, and turn activity into execution.

Three implications stand out.

Governance is now a moving target. Anthropic released Fable 5 to the public while deliberately gating Mythos 5 and routing sensitive cyber, biology, and chemistry queries to a more constrained model. A frontier lab choosing to release one model and restrict another is a live example of the governance question every regulated institution now faces. Risk frameworks built for annual cycles are being tested by capabilities that change month to month.

Performance is being redefined. When one professional with the right tools can do what once took a team, the definition of high performance changes. This is the heart of CFTE’s work on the AI-fication of Jobs and the emergence of what we call Supercharged Professionals, people who combine deep domain judgment with real AI leverage.

The workforce will split three ways at once. Using the CDE Innovation Prism, a framework for how technology reshapes work, we expect three patterns to happen simultaneously: displacement where AI competes directly with tasks, a small number of creative disruptors, and a growing class of supercharged professionals. The dividing line between them is not access to AI. It is the capability to use it well.

Prepare before the next Mythos moment

CFTE has been preparing financial services for this shift for years. We identified AI as the single most important technology impacting financial services in 2018, and have built the frameworks and programmes to turn that conviction into institutional capability ever since.

The gap that decides who thrives in exponential AI is capability, and capability can be built deliberately.

CFTE co-founder Huy Nguyen Trieu and Professor Douglas Arner of the University of Cambridge recorded a free 90 minute course, Exponential AI and the Impact on Financial Services, before today’s launch. It explains the Three Waves of AI, the forces compounding them, how regulators are responding, and how individuals and institutions can move from watching to building. It is free and includes a certificate.

Access the course and find more information here: https://courses.cfte.education/exponential-ai-and-the-impact-on-finance/

Frequently asked questions

Is Claude Mythos available to the public?

No. The public model is Claude Fable 5, a Mythos-class model made safe for general use. Claude Mythos 5, the version with cybersecurity safeguards lifted, is restricted to selected partners through Project Glasswing and the US Government.

What is exponential AI?

Exponential AI describes the pattern of AI becoming sharply more capable and sharply cheaper at the same time, year after year, so that what is impossible today can become routine in a matter of months rather than decades.

Why does the Fable 5 launch matter for finance?

Because the most capable AI model ever made public is now available to every institution and professional, at less than half the previous price, and it can complete complex work rather than only assist with it. This compresses the gap between large and small institutions and tests governance frameworks built for slower change.

Why does CFTE call this a signal of Exponential AI?

Because the release is not only about one model. It shows a broader pattern: AI capability is improving faster, becoming more widely available and affecting more complex tasks. That is the core idea behind Exponential AI.

The post Mythos Has Arrived: Why Financial Services Must Prepare for Exponential AI appeared first on CFTE.

Artificial intelligence is rapidly reshaping financial services. From credit scoring and insurance pricing to fraud detection, algorithmic trading, compliance and wealth management, AI is becoming embedded in the systems that influence how financial institutions operate and how customers access financial products.

But as AI becomes more powerful, one question is becoming increasingly urgent: how can financial institutions unlock the benefits of AI while ensuring that it remains safe, fair, explainable and accountable?

This was the focus of CFTE’s latest AI and Finance webinar, delivered in collaboration with Global Women in AI, featuring Dr Amira Abdelaziz, Senior Advisor for Data Science and Advanced Analytics at the Central Bank of Egypt and Executive Committee member of Global Women in AI.

In her session, “Beyond the Horizon: Singularity, Finance and the Governance Imperative,” Dr Amira explored how the next stage of AI could transform finance, why governance can no longer be treated as optional, and what institutions can do now to prepare.

Why AI Governance Matters in Financial Services

Dr Amira opened the session with a powerful scenario: by 2030, an AI system could manage savings, price insurance, approve loans and make financial decisions — while its reasoning remains invisible to the people affected by those decisions.

This is the core challenge of AI governance in finance.

Financial services is built on data, risk, trust and regulation. Every transaction, credit decision, compliance check, customer interaction and investment recommendation generates information. This makes finance one of the sectors most ready for AI adoption.

It also makes finance one of the sectors most exposed if AI systems go wrong.

As Dr Amira explained, AI governance is no longer a “nice to have”. It is becoming essential because financial institutions need to know how AI systems are being used, what decisions they influence, what data they rely on, how they are monitored, and who is accountable when something fails.

Finance Is AI’s Native Environment

One of the strongest messages from the webinar was that finance is a natural environment for AI because it is a pure information business.

Every decision in finance can be quantified. Every risk can be measured. Every transaction creates a signal. AI systems are designed to detect patterns, process data at speed and support decision-making at scale.

This is why AI is already transforming multiple areas of financial services.

In algorithmic trading, AI systems can process news, market signals, social media sentiment and price movements in real time. In insurance underwriting, AI can reduce decision-making from weeks to seconds by analysing risk variables, fraud indicators and behavioural patterns. In credit scoring, AI can use alternative data to assess customers who may not have traditional credit histories. In fraud detection, AI can identify suspicious transactions faster than rule-based systems. In wealth management, robo-advisors can make investment guidance more accessible to wider groups of customers.

These use cases show the opportunity. AI can improve speed, efficiency, access and personalisation across financial services.

But they also show why governance is critical.

The Risks Financial Institutions Cannot Ignore

AI in finance brings significant benefits, but it also introduces risks that institutions must manage carefully.

Dr Amira highlighted several governance risks that financial institutions need to address:

Algorithmic bias can occur when AI systems are trained on historical data that already contains discrimination. If past lending or hiring decisions were biased, an AI system may learn and amplify those patterns.

The explainability gap becomes a major issue when AI decisions cannot be clearly explained to customers, regulators, auditors or even internal teams. In financial services, this is especially important because decisions can affect access to credit, insurance, savings and investment opportunities.

Concentration risk can emerge when many financial institutions depend on a small number of AI vendors or models. A vulnerability, update failure or model weakness could become a systemic risk.

Adversarial AI creates new security challenges. Bad actors may attempt to manipulate AI systems, poison data or design fraud patterns that are difficult for automated systems to detect.

Job displacement and workforce disruption also need to be considered, particularly as AI systems become capable of performing increasingly complex tasks across compliance, operations, legal, customer service and analysis.

The lesson is not that financial institutions should avoid AI. The lesson is that they must adopt AI responsibly.

Explainability Is Becoming a Trust Issue

A recurring theme throughout the webinar was explainability.

If an AI system denies a mortgage application, prices insurance differently for two customers or flags a transaction as suspicious, the institution must be able to explain why. Customers need to be able to challenge decisions. Regulators need to audit systems. Internal teams need to understand how models behave.

Dr Amira used real-world examples to show what happens when explainability is weak. In cases where institutions could not explain why a model produced a certain outcome, the issue became more than a technical problem. It became a regulatory, reputational and trust problem.

For financial institutions, explainability is no longer only about model transparency. It is about customer protection, compliance and accountability.

AI Governance Needs to Be Built from the Beginning

One of the clearest messages from the session was that governance must be embedded before AI systems scale.

Institutions cannot wait until after deployment to ask whether a model is fair, explainable or secure. Governance needs to be part of design, procurement, implementation, monitoring and review.

Dr Amira outlined five practical pillars of AI governance:

1. Accountability
   Organisations need a named AI risk owner or AI governance leader with a real mandate, not just a general department responsible for AI.
2. Transparency
   AI decisions must be logged and explainable, especially when they affect customers.
3. Fairness
   Bias testing should not happen once. Models drift over time, so audits need to be repeated regularly.
4. Security
   AI models should be treated with the same security importance as core banking systems.
5. Human oversight
   Humans must be able to intervene in high-impact decisions, especially in areas such as credit, insurance pricing and anti-money laundering.

A Practical 90-Day Roadmap for AI Governance

For institutions wondering where to begin, Dr Amira shared a practical 90-day roadmap.

In the first 30 days, organisations should conduct a full AI inventory. This means identifying every AI system being used across the institution, including tools embedded in third-party vendor contracts.

In the next 30 days, they should classify AI systems by risk. High-risk areas such as credit scoring, anti-money laundering and insurance pricing require stronger oversight, documentation and explainability.

In the final 30 days, institutions should begin to act. This includes appointing an AI risk owner, drafting an AI policy, implementing explainability logging for high-risk decisions and scheduling regular model audits.

The key message was simple: you cannot govern what you cannot see.

Emerging Markets Need Their Own AI Governance Approaches

Another important insight from the webinar was the need for AI governance frameworks that reflect the realities of emerging markets.

Many global AI governance frameworks have been developed from high-income, Western regulatory contexts. They often assume strong digital identity infrastructure, mature credit bureau systems, advanced supervisory capacity and well-established legal mechanisms.

But many financial institutions and regulators in emerging markets operate in very different environments.

Dr Amira highlighted that this should not be seen only as a challenge. Emerging markets also have an opportunity to develop innovative governance models that reflect local needs, financial inclusion goals, multilingual environments, sparse data contexts and mobile-first financial ecosystems.

This is especially important as AI has the potential to expand access to finance for people who have historically been excluded from traditional banking.

What This Means for Finance Professionals

For CFTE, this webinar reinforced a key point: the future of finance will not only be shaped by technology, but by the people who understand how to use it responsibly.

AI governance is becoming a core capability for financial services professionals. It is relevant not only for data scientists and technology teams, but also for leaders, regulators, compliance professionals, risk teams, product managers, legal teams and customer-facing functions.

As AI becomes embedded across financial services, professionals will need to understand not only what AI can do, but how it should be governed.

Conclusion: The Future of Finance Requires Responsible AI

AI is already changing financial services. The next stage will bring even greater speed, scale and complexity.

The institutions that succeed will not simply be those with the most data or the most advanced models. They will be those with the strongest governance, the clearest accountability and the ability to use AI responsibly.

As Dr Amira reminded participants, the future of AI in finance is not something to fear. It is something to prepare for.

At CFTE, we are committed to helping financial institutions, regulators and professionals build the knowledge and capabilities needed for this next chapter of finance.

The post AI Governance in Finance: Why Responsible AI Is Now a Strategic Priority appeared first on CFTE.

Mythos alarmed regulators, central banks and bank leaders globally. The institutions treating it as a one-off event are misreading the situation. AI capability is compounding. Deployment cost is collapsing. Financial services was built for linear change. This post explains the shift, what it demands across four dimensions of readiness, and what professionals and institutions should do before the next wave arrives.

The Alarm Went Off. Most Institutions Pressed Snooze.

When Mythos was released, the reaction was immediate. The BBC, the New York Times, Reuters and the Financial Times all covered it. Regulators convened emergency meetings. Central banks issued warnings. The world’s most senior financial policymakers put it on the agenda.

And then, gradually, the conversation is losing its steam.

This is precisely the wrong response.

Mythos matters not because of what it is. It matters because of what it signals. A new class of AI systems that can reason, plan, use tools and act with increasing autonomy has arrived in financial services. If Mythos is the first visible signal, the question the industry should be asking is whether it is ready for the next ten.

At CFTE, we have been preparing financial services professionals and institutions for this moment since 2018, when we identified AI as the single most important technology reshaping the sector. What we are witnessing now is not a moment to observe. It is a moment to prepare.

Exponential AI: Why Linear Thinking Will Fail You

Most institutions are making a category error. They are tracking AI development the way they track everything else: reviewing quarterly model releases, updating internal policies in annual cycles, running pilot programmes before committing to adoption.

This is the wrong mental model.

AI capability is not growing linearly. It is compounding. Each generation of models does not add incremental capability. It multiplies it. Simultaneously, the cost of deployment has collapsed. What required enterprise infrastructure and seven-figure technology budgets two years ago is now accessible at a fraction of the cost.

The result is a structural asymmetry that financial services has not encountered before. The sector operates on regulatory cycles, planning horizons, risk frameworks and workforce development programmes calibrated for a predictable environment. Exponential AI is not predictable.

This is what CFTE calls the Exponential Preparedness Gap.

AI capability is advancing faster than institutions, regulators and professionals are adapting their governance, operating models, risk frameworks and skills. Without structural intervention, the gap widens with every capability jump.

The Three Waves of AI: Where We Are Now

The First Wave: Machine Learning and Prediction

The first wave gave institutions the ability to analyse large datasets and predict outcomes with greater accuracy. Credit scoring, fraud detection and risk modelling were transformed. AI as a tool that enhanced existing processes.

The Second Wave: Generative AI

The second wave gave professionals the ability to write, code, synthesise and assist at scale. This wave was characterised by AI as a collaborator in professional work.

The Third Wave: Agentic and Autonomous AI

This is the wave Mythos represents. Systems that can reason across multiple steps, use external tools, execute tasks and operate with reduced human oversight. They do not just respond to instructions. They pursue objectives.

For financial services, where trust, resilience and accountability are structural requirements, this does not represent an upgrade to the previous wave. It redefines what governance, risk exposure and professional capability require.

What This Means Across Four Dimensions

Exponential AI reshapes financial services simultaneously across four interconnected dimensions. Each one demands a response.

Work. The task and job layer is changing. Agentic AI can now execute the multi-step cognitive workflows that previously required skilled professionals: drafting credit assessments, synthesising regulatory filings, running scenario analyses.

The question is not whether your institution’s task structure will change. It is whether you have mapped which roles face the greatest exposure and what your people need to develop.

The Performance Hexagon, developed by Huy Nguyen Trieu, identifies the professionals most at risk as Task Robots: those who execute process without applying judgement. The transition to Problem Solver, System Thinker and ultimately Superstar is not optional.

Capability Infrastructure. Policy statements and pilot programmes are not enough.

Institutions need the governance architecture, oversight frameworks and workforce systems that allow them to deploy and learn from AI at scale. The CDE Innovation Prism provides the strategic lens for this work, asking not just what AI makes faster and cheaper, but what it makes genuinely new. Most institutions are still in efficiency mode. The third wave demands more.

Measurement. Most institutions know they need AI capability. Very few can define it with precision or measure it at scale.The CFTE AI Proficiency Framework defines three levels: AI Literacy (L1), Applied AI Practitioner (L2) and AI Systems and Decision Leader (L3),
across ten capability domains. Without a measurement standard, capability development is guesswork. The Exponential Preparedness Gap cannot be closed without first knowing how wide it is.

The Individual. The individual answer to the Exponential Preparedness Gap is the Supercharged Professional: someone who has deeply integrated AI capability into their professional practice, applies AI tools with judgement and continues developing as the capability landscape evolves. Not a one-time course. A professional orientation.

What Regulators Are Already Doing

The regulatory response to Mythos has been the fastest, most coordinated reaction to a technology development that financial supervisors have produced in years. The evidence is worth examining closely, because it signals where institutional expectations are heading.

The Financial Stability Board. Bank of England Governor Andrew Bailey, who chairs the FSB  the global risk watchdog coordinating financial rules for G20 economies  requested directly that Anthropic brief the FSB’s leading finance ministries and central banks on the cyber vulnerabilities Mythos had identified.

At a speech at Columbia University in April, Bailey warned the model could “crack the whole cyber risk world open.” The FSB confirmed it “welcomes engagement with Anthropic and other firms on emerging and frontier risks to global financial stability.” (Reuters, May 2026)

The ECB. On May 24, 2026, the European Central Bank convened an ad hoc emergency meeting rare by the ECB’s own standards specifically to address cybersecurity risks from the new generation of AI models. ECB Executive Board member Frank Elderson told the Financial Times: “This is something that is game-changing. We want banks to look into this seriously. The clock is ticking.” His framing was unambiguous: banks need to move from andante to presto. (FT,  May 2026)

The US Federal Reserve and Treasury. Treasury Secretary Scott Bessent and Federal Reserve Chair Jerome Powell convened an emergency meeting at Treasury’s Washington DC headquarters with the CEOs of the banks the Fed classifies as structurally important to the global financial system. The specific purpose was to ensure banks were prepared to defend against the cybersecurity risks identified by Mythos. (Bloomberg, April 2026)

Not being part of the response is not a neutral position. It is a risk that will now be visible to supervisors across multiple jurisdictions simultaneously.

The Question Every Leader in Financial Services Should Ask Today

Here is the honest question: if Mythos is the first signal, and three more capability jumps of comparable magnitude arrive in the next 18 months, is your institution prepared to govern them?

Not to stop them. To govern them. To understand what changes, to assess the exposure, to adapt the operating model and to maintain the trust of clients, regulators and the public that financial services depends on.

There are four things every leader should be able to answer right now:

One. Have we mapped which roles and tasks in our institution face the greatest exposure to the third wave? Or are we operating blind?

Two. Do we have a governance framework that is designed for AI systems that change faster than annual review cycles? Or are we applying model risk management designed for a static world to a dynamic one?

Three. Can we measure the AI proficiency of our workforce with precision? Do we know where the gaps are and what they cost us? Or are we guessing?

Four. Are our leaders and professionals oriented toward continuous capability development? Or do we treat AI learning as a one-time compliance activity?

If the honest answer to any of these is unclear, the gap is structural. And the time to close it is before the next signal arrives, not after.

Where to Start?

CFTE built this course because the industry needed it – Exponential AI and the Future of Financial Services, available without any paywall. No registration barrier.  

This was a deliberate decision. Exponential AI affects every level of financial services: bank leaders, regulators, risk and compliance teams, HR and L&D functions, technology teams, professionals at every stage of their career.

The more people inside financial institutions understand what is happening and why, the better the sector’s collective response will be. That outcome is too important to put behind a paywall.

The course is 90 minutes. It is taught by two of the most credible voices in this space.

Huy Nguyen Trieu, Co-Founder and CEO of CFTE, former Managing Director at Citi and Associate Fellow at Oxford Said, co-created one of the world’s largest Fintech courses, reaching 140,000 participants across 130 countries. He developed the frameworks referenced throughout this post: the CDE Innovation Prism, the Performance Hexagon and the AI Capability Engine. In the course, he builds the exponential capability evidence base, the Three Waves framework and the strategic lens for what institutions and professionals should do next.

Professor Douglas Arner, Kerry Holdings Professor in Law at HKU and Associate Director at Cambridge CCAF, is one of the foremost authorities on finance, technology and regulation. He addresses the regulatory and governance response to Exponential AI: what frameworks exist, where the gaps remain, and what practical steps firms and supervisors should take.

The course covers six areas:

• Why AI progress is exponential and what that means structurally;
• The Three Waves and where the third wave sits today;
• The CDE Innovation Prism applied to financial services strategy;
• How regulators are responding and where the institutional tensions remain;
• What leaders must do before the next capability jump arrives; and
• How individuals can begin the transition to becoming Supercharged Professionals.

It is designed to be used as a shared institutional briefing resource. If you are a leader, share it with your team. If you work in risk, compliance, HR or L&D, forward it to the people who need to understand this before your organisation’s next governance or strategy cycle.

The next Mythos moment is not a question of if. It is a question of when. The institutions and professionals who have done the foundational thinking will be the ones who respond with clarity rather than react with alarm.

• Start the free course here.
• Share the course with your organisation.

Further Reading

• CFTE AI Proficiency Framework — The measurement standard for AI proficiency in financial services
• Supercharged Professional Programme — The individual capability pathway for professionals navigating the third wave
• Senior Leadership Alignment Programme — Institutional alignment for leaders navigating Exponential AI
• AI Capability Diagnostic — Institutional assessment of AI readiness gaps across ten capability domains
• The AI-fication of Talents Whitepaper – CFTE’s foundational research on how AI transforms professional capability requirements

Structured Summary for Reference

What is Exponential AI in financial services? Exponential AI refers to the compounding acceleration of AI capability combined with the collapse in deployment cost, producing a pace of change that financial institutions, regulators and professionals were not structured to absorb. It is characterised by the arrival of agentic AI systems — such as Mythos — that can reason, plan and act autonomously.

What is the Exponential Preparedness Gap? The Exponential Preparedness Gap, a concept developed by CFTE Co-Founder Huy Nguyen Trieu, describes the widening distance between the pace of AI capability advancement and the speed at which institutions, regulators and professionals are adapting their governance, operating models, risk frameworks and skills.

What are the Three Waves of AI in financial services? The first wave was machine learning and prediction. The second wave was generative AI. The third wave, now arriving, is agentic and autonomous AI: systems that can reason across steps, use tools and act with limited oversight. Mythos is a signal of the third wave.

What is the CDE Innovation Prism? The CDE Innovation Prism is a strategic framework developed by Huy Nguyen Trieu that classifies AI impact as Cheaper/Better/Faster (C), Enhancing (E) or Different (D). It helps institutions move beyond efficiency thinking to assess what Exponential AI makes genuinely new.

CFTE works with more than 100 organisations including central banks, regulators, financial institutions and governments across 130+ countries. CFTE is AI Capability Infrastructure for financial services.

The post Mythos Got the Headlines. Exponential AI Is the Real Story Reshaping Financial Services. How to Prepare Before the Next Mythos Moment. appeared first on CFTE.

In early 2026, Mythos sent alarm bells across central banks, regulators, and financial institutions worldwide. Not because of how it became known, but because of what it could do.

Mythos was not designed as a cybersecurity tool. It was a general purpose model, the next step up from what millions of people use daily. Yet it could autonomously find and exploit vulnerabilities across virtually every major operating system and browser, without any human guidance, at a level exceeding the best human experts.

If a general purpose model could do this as a byproduct of its broader capabilities, what does the next generation look like?

Mythos is not the story. It is the signal.

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Huy Nguyen Trieu

Co-founder, CFTE
Author, The AI-fication of Jobs
Former Managing Director, Citi

Prof. Douglas Arner

Kerry Holdings Professor in Law, University of Hong Kong
Associate Director, Cambridge Centre for Alternative Finance

Huy Nguyen Trieu and Professor Douglas Arner have spent decades working at the intersection of finance, technology, regulation, and innovation. Both believe the world is now entering a fundamentally new phase of AI. To help finance professionals understand what this shift means, and what to do about it, CFTE has made this course free.

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The deeper issue

Exponential AI

Mythos points to something much larger. We have entered a third wave of AI, where systems can not only think and reason but act autonomously in the digital world. At the same time, AI is becoming ten to a hundred times more powerful and cheaper every year. What is impossible today could be possible tomorrow. Not tomorrow as a figure of speech. Tomorrow.

For financial services, this matters more than in almost any other sector. The industry is built on trust, risk management, regulation, and professional judgement. These are precisely the things exponential AI puts under pressure. Yet most firms and professionals are still responding incrementally. The sector had a decade to adapt to fintech. It has had roughly three years to move from AI to generative AI, to reasoning AI, to agentic AI. The pace will not slow down.

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What the course covers

Huy opens by unpacking what Mythos revealed and why it signals something far larger. He introduces the three waves of AI, the concept of Exponential AI, and what it means for individuals and organisations to become anti-fragile in this environment.

Professor Arner then examines the implications for financial services specifically: why the sector was well positioned for the first wave, why that is no longer sufficient, and how different categories of risk, from malicious actors to infrastructure concentration to the loss of human expertise, require fundamentally different governance responses.

1.5 hours Self-paced Free

What you will learn

Key themes covered in the course

	What Mythos reveals about the third wave of AI and why it signals something much larger
	Why exponential AI makes linear planning obsolete and what a new strategic mindset looks like
	How to become a supercharged professional and why talent, not technology, is now the real differentiator
	The five categories of AI risk in financial services and why each requires a different governance response
	How institutions can move from AI-busy to AI-ready by rethinking their operating model

Start the course today

1.5 hours · Self-paced · Free

Enrol now →

The post Exponential AI and Financial Services: A Free Course appeared first on CFTE.

Last week, Tram Anh Nguyen, Co-Founder of CFTE and Founder of Global Women in AI, joined an important conversation hosted by 100 Women in Finance and the Barclays Innovation Hub powered by Eagle Labs in London.

The event, “Practical AI Adoption for Female Founders in FinTech,” brought together founders, industry leaders and ecosystem builders to explore what practical AI adoption means for financial services and fintech today. The discussion, alongside Rachel Tshondo, Emanuela Vartolomei and Ally Clegg, highlighted a key shift now taking place across the industry.

AI is no longer just a technology conversation. It is becoming an operating model conversation.

For organisations and founders, AI-readiness cannot be reduced to sending one person to an AI training programme and expecting them to return with a few useful tools. Nor is it simply about deploying more agents, automating individual tasks or experimenting with isolated use cases.

AI adoption requires a deeper organisational capability. It has to shape how decisions are made, how risks are governed, how products are built, how customers are served, how teams scale and how trust is created.

This means moving from AI awareness to AI capability.

For AI to be adopted responsibly and effectively, organisations need to build the foundations that allow it to scale. These include:

• AI-readiness assessments across the organisation
• AI literacy at scale
• Systems thinking skills
• Strong AI governance
• Transparency and accountability
• Trusted ecosystems
• Access to networks, markets and capital

For female founders in fintech and AI, this conversation is particularly important.

The barriers to building are falling. Domain experts can now prototype, automate, analyse, test and reach customers faster than ever before. AI is creating new opportunities for entrepreneurs to move from idea to execution with unprecedented speed.

However, access remains critical.

If access to capital, data, platforms and networks remains concentrated in the hands of a few, AI will not democratise opportunity. Instead, it risks accelerating existing gaps.

This is why practical AI adoption must also be connected to inclusion. The future of AI will be shaped not only by technology, but by who has the opportunity, skills, confidence and support to build with it.

Through Global Women in AI, Tram Anh and the wider community are working to help close this gap and support the next generation of women leaders in AI, including through the Supercharging 10,000 Women to Lead in AI programme.

Through CFTE, we continue to build the capability infrastructure that enables professionals, founders and institutions to move from AI awareness to AI leadership.

The future of AI should not be shaped by a narrow group of people. It must be shaped by a diversity of talent, experience and perspectives that reflect the world AI is meant to serve.

Thank you to 100 Women in Finance and the Barclays Innovation Hub powered by Eagle Labs teams for convening such an important and timely discussion.

The post AI Adoption Is Becoming an Operating Model Challenge appeared first on CFTE.

London, 22 May 2026 

CFTE, the Centre for Finance, Technology and Entrepreneurship, today announced the appointment of Dr David R. Hardoon as Senior Advisor, strengthening its work on AI governance, responsible adoption and capability building for financial institutions, regulators, central banks and governments.

As artificial intelligence moves from experimentation to implementation, financial institutions face a growing challenge: how to govern AI responsibly while building the people, systems and operating models required to use it effectively at scale. The question is no longer only whether institutions should adopt AI. It is how they build the capabilities, judgement and governance structures required to make AI adoption safe, useful and scalable.

Dr Hardoon brings more than two decades of experience across data, artificial intelligence, financial regulation, banking, government, academia and enterprise transformation. He was the Monetary Authority of Singapore’s first appointed Chief Data Officer and Head of Data Analytics Group, and subsequently served as Special Advisor for Artificial Intelligence. In these roles, he contributed to the development of AI strategy for MAS and Singapore’s financial sector, and was closely involved in the development of the FEAT principles and the MAS-backed Veritas consortium.

“I am delighted to welcome David Hardoon as Senior Advisor to CFTE. We have had the privilege of working with David for many years, and his expertise across policy, banking, technology, AI governance and ethics is exceptional. At a time when financial institutions and regulators are navigating one of the most important technological shifts of our generation, his perspective will be invaluable. David’s appointment strengthens CFTE’s mission to help institutions and leaders build the capabilities, confidence and responsible practices.”

Tram Anh Nguyen, Co-Founder, CFTE

He has also held senior data and AI leadership roles across financial services and enterprise innovation, including Global Head of AI at Standard Chartered, Chief Data and AI Officer at UnionBank of the Philippines, and Chief Executive Officer of Aboitiz Data Innovation.

From AI Governance to AI Capability at Scale

Dr Hardoon’s appointment comes at a time when governance has become one of the most important questions in financial services. Institutions are developing policies, principles and controls for AI, but governance can only work if it is translated into the daily behaviours, capabilities, workflows and decisions of the organisation.

Through his advisory role, Dr Hardoon will support CFTE’s work in helping institutions move from AI awareness and experimentation towards structured AI capability. His expertise will contribute to CFTE’s work across executive education, AI governance, responsible innovation, AI proficiency, institutional readiness and capability-building programmes for financial institutions, regulators and public-sector organisations.

The appointment also reinforces CFTE’s broader work on AI Capability Infrastructure, which focuses on helping institutions develop the people system and organisation system required to thrive in the AI era. This includes leadership alignment, AI capability diagnostics, role-based pathways, responsible use, operating model thinking and measurement systems that allow organisations to move from AI ambition to execution.

“I am delighted to join CFTE as Senior Advisor. Drawing on my experience pioneering the FEAT principles at MAS and leading enterprise AI strategy and governance at Standard Chartered, I am excited to help CFTE equip financial institutions with the human capability and responsible governance frameworks essential for safe, scalable and ethical AI deployment.”

Dr David Hardoon, Senior Advisor, CFTE

Supporting Responsible AI Adoption in Financial Services

CFTE works with financial institutions, regulators, central banks and governments globally to build capabilities in finance, technology and artificial intelligence. Its work increasingly focuses on helping institutions move from AI ambition to AI Capability Infrastructure: the practical systems, pathways and measurement mechanisms required to make AI adoption real, responsible and scalable.

Dr Hardoon’s appointment will also support CFTE’s institutional programmes, including senior leadership AI programmes, AI governance pathways, AI capability diagnostics and AI Academy initiatives for financial institutions, regulators and governments.

Dr Hardoon’s appointment will strengthen CFTE’s ability to support institutions as they address some of the most important questions in AI adoption:

• How should institutions govern AI while still enabling innovation?
• How can financial services leaders build responsible AI capability across teams and functions?
• How can regulators, central banks and financial institutions develop the internal capabilities required to understand, supervise and apply AI?
• How can AI governance be translated into practical behaviours, role-based capabilities and operating model decisions?
• How can organisations move from pilots and experimentation to responsible implementation at scale?

The appointment forms part of CFTE’s wider body of work on the future of finance, including AI-fication of Work, AI Capability Infrastructure, the CFTE AI Proficiency Framework and the development of Supercharged Professionals.

Notes to Editors

About Dr David R. Hardoon

Dr David R. Hardoon is an internationally recognised leader in data and artificial intelligence, with experience across financial regulation, banking, government, academia, advisory and enterprise transformation. He was the Monetary Authority of Singapore’s first appointed Chief Data Officer and Head of Data Analytics Group, and subsequently served as Special Advisor for Artificial Intelligence.

He has contributed to the development of AI strategy for MAS and Singapore’s financial sector, and has been closely involved in initiatives related to responsible AI, including the FEAT principles and the MAS-backed Veritas consortium. He has also held senior leadership roles including Global Head of AI at Standard Chartered, Chief Data and AI Officer at UnionBank of the Philippines, and Chief Executive Officer of Aboitiz Data Innovation.

Dr Hardoon holds a PhD in Computer Science in the field of Machine Learning from the University of Southampton and a First Class Honours BSc in Computer Science and Artificial Intelligence from Royal Holloway, University of London.

About CFTE

The Centre for Finance, Technology and Entrepreneurship (CFTE) is one of the world’s leading capability platforms in finance, technology and AI. Since 2017, CFTE has helped more than 200,000 participants across 130 countries build capabilities in fintech, digital finance, AI and the future of work.

CFTE works with financial institutions, central banks, regulators, technology firms, universities, development institutions and public-sector organisations to build capability at scale. Its work combines industry expertise, practitioner-led learning, proprietary frameworks and scalable programme design.

CFTE’s work on AI focuses on helping institutions move from AI awareness and experimentation to AI Capability Infrastructure: the systems, pathways and capabilities required to make AI adoption useful, responsible and scalable.

Media Contact

Email – partners@cfte.education
Website – https://www.cfte.education/

The post CFTE Appoints Dr David R. Hardoon as Senior Advisor to Strengthen AI Governance and Capability Building in Financial Services appeared first on CFTE.

By Tram Anh Nguyen Co-founder, CFTE (Centre for Finance, Technology and Entrepreneurship) | Chairwoman, Global Women in AI Closing keynote delivered at the 60th Women in Payments UK-EU Symposium, London, 2026

 

I have spent the past twenty-five years reinventing myself. 

From capital markets trader on Wall Street to wealth manager in London. From banking professional to co-founder of one of the world’s largest fintech education platforms. From building a company to building a global movement for women’s leadership in artificial intelligence.

Each of those transitions looked different from the outside. But from the inside, they all required the same thing: the willingness to rebuild, not just move.

I spoke at the closing of the 60th Women in Payments UK-EU Symposium in London because I believe we are standing at the most important professional turning point of our generation. Not just in payments. Not just in financial services. In every field that AI will touch, which is every field there is.

And my message is this: the professionals who will lead through this moment are not the ones who know the most about AI today. They are the ones who have built the capacity to reinvent themselves continuously.

Reinvention is not a moment. It is a muscle.

From Wall Street to CFTE: 

I began my career in emerging markets trading rooms in New York. They were fast, demanding, and overwhelmingly male. I then moved through Standard Chartered and Dresdner before joining UBS in London, working in wealth management with ultra-high-net-worth clients across the US, the UK, India and Asia.

At the time, I thought I had already learned how to reinvent myself. I had changed cities, institutions, asset classes and client bases. I had crossed cultures and time zones more times than I could count. I am of Vietnamese origin, born and raised in Paris, and my heritage has always shaped how I understand resilience and how I think about opportunity.

But those were career moves.

The first genuine reinvention came when I attended a session at Oxford almost by chance. The session was on fintech, taught by Huy Nguyen Trieu, who later became my co-founder. For the first time, I saw finance through a different lens. I saw that technology could make it more accessible, more inclusive, more efficient. I saw open banking, digital payments, mobile money and data platforms not as products, but as forces capable of changing lives.

And then I saw a gap.

The technology was accelerating. The industry was restructuring. But professionals were not learning fast enough. My former colleagues across capital markets, wealth management, compliance, regulation and payments were all asking the same questions: How do I keep up? How do I learn without going back to university, spending a fortune, or leaving my job?

The problem was not ambition. The problem was access.

That was the beginning of CFTE. Not as a business plan. As a response to a structural gap between those who had access to the future and those who did not.

The decision to build CFTE was not a career move. It asked me to rebuild my identity entirely. To go from having a title to having a mission. From a salary to uncertainty. From working inside an institution to questioning whether institutions were building the right things at all.

That is reinvention. And it does not happen without difficulty.

What Crisis Taught Me About the Reinvention Muscle

On the morning of 11 September 2001, I was in New York. Our office was in World Trade Center 7, and we lived directly across from the Twin Towers.

I was walking to work when the first plane hit.

Within minutes, the phone networks were down, the streets were chaos, and I could not return to my apartment. My husband was in Paris. I was alone, displaced and completely uncertain about what would follow.

In the weeks that followed, our team at Standard Chartered moved to temporary offices in New Jersey. We rebuilt the trading room. We supported one another. We created an environment where people could feel safe, work, and believe in the future again. One year later, our team was recognised as one of the best on Wall Street.

That experience embedded something in me that has never left: a crisis does not build the reinvention muscle. Choosing to rebuild does.

You may not choose the disruption. But you choose the response.

When COVID arrived, CFTE was still a young company. Travel stopped. Events stopped. Filming stopped. Projects were delayed. Clients were uncertain. Teams were under pressure.

And once again, the question was the same: how do we rebuild?

This time, I had something I did not have in 2001. I had already practised reinvention. I had already learned to move fast, to operate efficiently, to find new ways to deliver value under constraint.

We created virtual campuses. We supported thousands of professionals stuck at home, anxious about their futures, hungry to learn. CFTE did not just survive that period. According to our own platform data, CFTE trained over 200,000 professionals across more than 130 countries in the years that followed, with demand accelerating through and after COVID.

Not because everything was in place. Because the team had already built the muscle.

The Three Waves of AI and Why the Third One Changes Everything

Today, CFTE has worked with central banks, regulators, global financial institutions and fintech companies around the world. But the reason I care so deeply about what is happening with AI right now is not about CFTE’s growth.

It is because we are at another inflection point. And this one is structurally different from anything the past decade produced.

I think about AI in three distinct waves.

Wave 1: Predictive AI

The first wave was AI that could detect patterns. Fraud detection. Credit scoring. Recommendation engines. This was powerful, but largely invisible. It operated behind the system and was inaccessible to most professionals without deep technical training.

Wave 2: Cognitive Assistance

The second wave changed the interface. AI could draft, summarise, translate, generate, research and code. It became a collaborator that any professional could access. This is the wave most of us have been operating in for the past two to three years.

Wave 3: Cognitive Autonomy and Execution

We are now entering the third wave. AI is no longer answering questions. It is taking actions. Moving from tools to agents. From support to execution. From assisting professionals to, in some cases, replacing discrete tasks entirely.

When the internet was growing at 2,300 percent annually in 1994, Jeff Bezos left a well-paid hedge fund role because he understood what exponential growth actually means. The internet took twelve years to reach 800 million users. AI reached that number in three years.

This is the world we are entering. And every professional in finance, payments and regulation will need to reinvent how they work within it.

The Paradox at the Centre of This Moment

And yet, here is the tension I cannot ignore.

In a world of abundant AI tools, platforms and opportunity, out of the top 100 leaders in artificial intelligence today, only 10 are women.

The rooms where AI products are built, where investment decisions are made, where governance models are written, where the standards and ethics of AI are being defined: these rooms are still not representative.

This is not a pipeline problem. This is a structural one.

Women across finance, payments, regulation and entrepreneurship have the domain expertise, the institutional knowledge and the leadership experience that the AI era needs. What is missing is not capability. What is missing is access to the infrastructure of AI leadership.

That is precisely why I founded Global Women in AI, a multi-stakeholder platform established to advance women’s leadership, representation and influence across the artificial intelligence ecosystem. Operating across more than 100 countries, and working with governments, industry leaders, academic institutions and international bodies, the platform is building the infrastructure for women to shape AI development, not simply adopt it.

As part of this work, I am proud to be building the Supercharged 10,000 Women in AI initiative, designed to equip 10,000 women with the mindset, skills, confidence, network and practical AI fluency to lead in the age of cognitive autonomy. Not just to use AI tools, but to build with AI, govern AI and lead organisations through AI-driven transformation.

The reinvention pipeline for women in AI is not empty. It is blocked. And we are working to unblock it.

Three Things I Have Learned About Building the Reinvention Muscle

After twenty-five years of navigating change across banking, entrepreneurship and AI, I would share three lessons with any professional standing at this particular crossroads.

Your past is not a disadvantage. It is your edge.

When I moved from banking into fintech education, I did not abandon my experience. I built on it. My understanding of institutional finance helped me work with regulators and central banks. My experience as one of the few women in the trading room helped me understand why access and inclusion are structural issues, not personal ones. Do not assume your background makes you late to AI. Your domain expertise may be precisely what makes you valuable within it. The future is not built by people who discard their knowledge. It is built by people who upgrade it.

Reinvention is not something you build alone.

No significant transformation happens in isolation. The Women in Payments community exists because women created space for each other. Networks matter. Communities matter. Sponsorship matters. When Sir Demis Hassabis and the team at Google DeepMind built AlphaFold, using AI to predict more than 200 million protein structures and earning the 2024 Nobel Prize in Chemistry, they were working as a team, within an institution, with accumulated knowledge from across a global scientific community. AI did not replace their curiosity. It amplified it. The same is true for every professional in this room who chooses to build collaboratively rather than wait for permission to lead.

The future rewards those who build.

The greatest professional advantage in a world that is accelerating is not knowing everything. It is the capacity to learn fast, adapt fast and build fast. In finance, in payments, in regulation, in technology, the professionals who will define the next decade are already asking the question: where can I reinvent myself to create something that did not exist before?

A Closing Thought

Reinvention is a muscle.Some of you have used it many times already. Some of you are using it now. Some of you will need to use it very soon.

My message is the same as it was when I stood on that stage in London: do not wait to be invited into the future. Build your place in it. The AI era is not something that is happening to you. It is something you are capable of shaping. And in a world that moves faster than any of us planned for, the most important thing any of us can do is keep building.

Tram Anh Nguyen is Co-founder of CFTE (Centre for Finance, Technology and Entrepreneurship), a global platform that has trained over 200,000 finance and technology professionals across 130+ countries, and Chairwoman of Global Women in AI.  

The post Women in AI Leadership and the Age of Reinvention: Reflections from the Women in Payments 2026 Symposium appeared first on CFTE.